Packets were transmitted over GE channels with burst loss probabilities \(P_B \in \lbrace 0.01, 0.10, 0.20, 0.30\rbrace \)  and average burst lengths of \(L_B \in \lbrace 1, 2, \cdots , 7\rbrace \) . The range of burst loss probabilities chosen for Monte Carlo experiments is in accordance with a conventional practice in video streaming research [1]}.
All tensor channels in our experiments are packetized into 7 packets: \(56 \times 56\)  tensor channels with 8 rows of tensor elements per packet (\(7\cdot 8=56\) ) and \(28 \times 28\)  tensor channels with 4 rows of features per packet (\(7\cdot 4=28\) ).
The range of \(L_B\)  was chosen up to 7, to cover the case where the entire tensor channel is likely to be lost. In our Monte Carlo experiments, for each pair of \((P_B,L_B)\)  values, 20 channels realizations were simulated for each batch of the 882 image test set. Boolean loss maps for each test batch over 20 channel realizations of each GE channel \((P_B,L_B)\)  were saved. These loss maps were then used to re-create packet loss patterns so that the same corrupted tensors were presented to each loss concealment method.
